The word “credit” originates from French, via Italian from the Latin credere, meaning “believe” or “trust”. In other words, credit is extended to people who can be believed or trusted to pay it back. In traditional financial services, credit scores represent the likelihood of a person repaying a loan and are compiled from information about an individual’s repayment behaviour, i.e. how they have historically managed previous loans or other financial products. It is their past behaviour that determines whether they should be trusted or not.
According to the World Bank, there are 2 billion people around the world, mostly in developing countries, who lack access to even the most basic banking services. Even in advanced economies, up to 20% of consumers’ credit history is either completely lacking or too thin to generate a traditional credit score. Given that the word credit relates to a person’s trustworthiness, it stands to reason that psychology may be a useful tool to assess a person’s inclination towards repaying debt according to their personality. This could be very useful, especially where information about repayment behaviour is absent.
Psychometric instruments are most commonly used within the human resources realm for candidate hiring and conducting assessments at different points of the employee cycle. In order to measure the psychometric traits that are linked to financial behaviour and credit risk, four models (MBTI, Ocean/Big 5, PCL-R, Hogan Personality Inventory) were used to cover the traits which characterise a good borrower. Intent to repay and financial conscientiousness were the main characteristics to be measured. The selected traits are supported by extensive primary research and pilot studies, for example, inhabitants of four different towns in Uttar Pradesh, rural India, provided their credit scores, played the game and underwent the MBTI test. The findings were very positive.
The idea originated from a chatbot, using NLP mapped to psychological elements (based on the Big Five model in psychology) to screen prospective rental tenants in the property sector. This prototype was tweaked to image-based selection in order to be scalable for multiple languages (for example, 207 dialects in India!) and actual gamification, which is much more engaging. ConfirmU had subsequently developed a short game, constituting psychometric tests that deliver an immediate credit score.
MAI modelling case study
The case study was performed on data pertaining to loans to small-scale dairy farmers in Kenya. The farmers played the game and the MAI data science team was able to achieve a high Gini coefficient (a measure of predictive strength) overall, as well as very high predictive values on out-of-time testing, indicating that the model generalises well. The strength of this psychometric model was comparable to models built with credit bureau data within the unsecured space.
Given the transparent modelling technique and the low level of correlation with other sources, the scorecard the MAI team built off psychometric data is a powerful tool for both thin and thick file customers. One of the weaknesses of a traditional “thin file” or “new to credit” scorecard is the tendency to lump many individuals of the same age or income together, whereas the additional psychometric lens considers them differently. The MAI scorecard displayed even deciles in the scored population and the bad rates of the riskiest decile were three times that of the bad rates of the low-risk decile with a steady change. In practice, this could translate into giving someone with a lower score a smaller amount, instead of just declining them, maximising not only profit but also impact and sustainability.
Traditional credit scorecards are adjusted for different locations because even financial data needs to be interpreted within a local context. It could be argued that one’s personality profile is strongly influenced by one’s culture. Therefore, a major unique selling point of the game is that it is customisable – it can be adapted to local culture to produce valid results, whether it is played in India, Kenya or Vietnam. A pilot study is conducted and a bespoke model is built for each new location and potential lenders. This is based on an understanding of the practicalities and the characteristics of the audience. A link is sent and a consumer plays the game, after which a model is constructed based on data from the game combined with loan repayment information. It would then be very easy to scale the model to other clients in the same market.
Credit and non-credit applications
The first pilot occurred among rural, micro-entrepreneurial women, who are representative of the microfinance industry. ConfirmU is currently penetrating the unique segment of agri-lending in Kenya amidst a booming digital landscape. In addition, localisation is underway to target a metropolitan, young, tech-savvy population without a credit history. Non-credit applications potentially include buy-now-pay-later platforms, a flourishing segment worldwide that is gaining more and more traction in emerging markets, as well as insurance and wealth tech. The largest credit bureau in the world aims to apply this solution in the property tech arena. There is also room for credit scoring in the blockchain DeFi (decentralised finance) space where collateral is required on lending platforms.
Using gamification for credit scoring, ConfirmU’s overall vision is to become a global alternative credit bureau. Traditional models are good, but they don’t actually evaluate the person’s intent to repay based on personality.